// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

template<typename DataType, int DataLayout, typename IndexType>
static void
test_simple_reverse(const Eigen::SyclDevice& sycl_device)
{
	IndexType dim1 = 2;
	IndexType dim2 = 3;
	IndexType dim3 = 5;
	IndexType dim4 = 7;

	array<IndexType, 4> tensorRange = { { dim1, dim2, dim3, dim4 } };
	Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
	Tensor<DataType, 4, DataLayout, IndexType> reversed_tensor(tensorRange);
	tensor.setRandom();

	array<bool, 4> dim_rev;
	dim_rev[0] = false;
	dim_rev[1] = true;
	dim_rev[2] = true;
	dim_rev[3] = false;

	DataType* gpu_in_data =
		static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_out_data =
		static_cast<DataType*>(sycl_device.allocate(reversed_tensor.dimensions().TotalSize() * sizeof(DataType)));

	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> out_gpu(gpu_out_data, tensorRange);

	sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(), (tensor.dimensions().TotalSize()) * sizeof(DataType));
	out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
	sycl_device.memcpyDeviceToHost(
		reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize() * sizeof(DataType));
	// Check that the CPU and GPU reductions return the same result.
	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 3; ++j) {
			for (IndexType k = 0; k < 5; ++k) {
				for (IndexType l = 0; l < 7; ++l) {
					VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(i, 2 - j, 4 - k, l));
				}
			}
		}
	}
	dim_rev[0] = true;
	dim_rev[1] = false;
	dim_rev[2] = false;
	dim_rev[3] = false;

	out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
	sycl_device.memcpyDeviceToHost(
		reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize() * sizeof(DataType));

	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 3; ++j) {
			for (IndexType k = 0; k < 5; ++k) {
				for (IndexType l = 0; l < 7; ++l) {
					VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(1 - i, j, k, l));
				}
			}
		}
	}

	dim_rev[0] = true;
	dim_rev[1] = false;
	dim_rev[2] = false;
	dim_rev[3] = true;
	out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
	sycl_device.memcpyDeviceToHost(
		reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize() * sizeof(DataType));

	for (IndexType i = 0; i < 2; ++i) {
		for (IndexType j = 0; j < 3; ++j) {
			for (IndexType k = 0; k < 5; ++k) {
				for (IndexType l = 0; l < 7; ++l) {
					VERIFY_IS_EQUAL(tensor(i, j, k, l), reversed_tensor(1 - i, j, k, 6 - l));
				}
			}
		}
	}

	sycl_device.deallocate(gpu_in_data);
	sycl_device.deallocate(gpu_out_data);
}

template<typename DataType, int DataLayout, typename IndexType>
static void
test_expr_reverse(const Eigen::SyclDevice& sycl_device, bool LValue)
{
	IndexType dim1 = 2;
	IndexType dim2 = 3;
	IndexType dim3 = 5;
	IndexType dim4 = 7;

	array<IndexType, 4> tensorRange = { { dim1, dim2, dim3, dim4 } };
	Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
	Tensor<DataType, 4, DataLayout, IndexType> expected(tensorRange);
	Tensor<DataType, 4, DataLayout, IndexType> result(tensorRange);
	tensor.setRandom();

	array<bool, 4> dim_rev;
	dim_rev[0] = false;
	dim_rev[1] = true;
	dim_rev[2] = false;
	dim_rev[3] = true;

	DataType* gpu_in_data =
		static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_out_data_expected =
		static_cast<DataType*>(sycl_device.allocate(expected.dimensions().TotalSize() * sizeof(DataType)));
	DataType* gpu_out_data_result =
		static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize() * sizeof(DataType)));

	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> in_gpu(gpu_in_data, tensorRange);
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> out_gpu_expected(gpu_out_data_expected, tensorRange);
	TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> out_gpu_result(gpu_out_data_result, tensorRange);

	sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(), (tensor.dimensions().TotalSize()) * sizeof(DataType));

	if (LValue) {
		out_gpu_expected.reverse(dim_rev).device(sycl_device) = in_gpu;
	} else {
		out_gpu_expected.device(sycl_device) = in_gpu.reverse(dim_rev);
	}
	sycl_device.memcpyDeviceToHost(
		expected.data(), gpu_out_data_expected, expected.dimensions().TotalSize() * sizeof(DataType));

	array<IndexType, 4> src_slice_dim;
	src_slice_dim[0] = 2;
	src_slice_dim[1] = 3;
	src_slice_dim[2] = 1;
	src_slice_dim[3] = 7;
	array<IndexType, 4> src_slice_start;
	src_slice_start[0] = 0;
	src_slice_start[1] = 0;
	src_slice_start[2] = 0;
	src_slice_start[3] = 0;
	array<IndexType, 4> dst_slice_dim = src_slice_dim;
	array<IndexType, 4> dst_slice_start = src_slice_start;

	for (IndexType i = 0; i < 5; ++i) {
		if (LValue) {
			out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
				in_gpu.slice(src_slice_start, src_slice_dim);
		} else {
			out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
				in_gpu.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
		}
		src_slice_start[2] += 1;
		dst_slice_start[2] += 1;
	}
	sycl_device.memcpyDeviceToHost(
		result.data(), gpu_out_data_result, result.dimensions().TotalSize() * sizeof(DataType));

	for (IndexType i = 0; i < expected.dimension(0); ++i) {
		for (IndexType j = 0; j < expected.dimension(1); ++j) {
			for (IndexType k = 0; k < expected.dimension(2); ++k) {
				for (IndexType l = 0; l < expected.dimension(3); ++l) {
					VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
				}
			}
		}
	}

	dst_slice_start[2] = 0;
	result.setRandom();
	sycl_device.memcpyHostToDevice(
		gpu_out_data_result, result.data(), (result.dimensions().TotalSize()) * sizeof(DataType));
	for (IndexType i = 0; i < 5; ++i) {
		if (LValue) {
			out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
				in_gpu.slice(dst_slice_start, dst_slice_dim);
		} else {
			out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
				in_gpu.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
		}
		dst_slice_start[2] += 1;
	}
	sycl_device.memcpyDeviceToHost(
		result.data(), gpu_out_data_result, result.dimensions().TotalSize() * sizeof(DataType));

	for (IndexType i = 0; i < expected.dimension(0); ++i) {
		for (IndexType j = 0; j < expected.dimension(1); ++j) {
			for (IndexType k = 0; k < expected.dimension(2); ++k) {
				for (IndexType l = 0; l < expected.dimension(3); ++l) {
					VERIFY_IS_EQUAL(result(i, j, k, l), expected(i, j, k, l));
				}
			}
		}
	}
}

template<typename DataType>
void
sycl_reverse_test_per_device(const cl::sycl::device& d)
{
	QueueInterface queueInterface(d);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_simple_reverse<DataType, RowMajor, int64_t>(sycl_device);
	test_simple_reverse<DataType, ColMajor, int64_t>(sycl_device);
	test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, false);
	test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, false);
	test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, true);
	test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, true);
}
EIGEN_DECLARE_TEST(cxx11_tensor_reverse_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		std::cout << "Running on " << device.get_info<cl::sycl::info::device::name>() << std::endl;
		CALL_SUBTEST_1(sycl_reverse_test_per_device<short>(device));
		CALL_SUBTEST_2(sycl_reverse_test_per_device<int>(device));
		CALL_SUBTEST_3(sycl_reverse_test_per_device<unsigned int>(device));
#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
		CALL_SUBTEST_4(sycl_reverse_test_per_device<double>(device));
#endif
		CALL_SUBTEST_5(sycl_reverse_test_per_device<float>(device));
	}
}
